Seeking a Developer Experienced with OpenAI, LangChain and Pinecone for AI Project

The primary objective of this project is to develop a robust system capable of converting uploaded document collections (in PDF or text format) into vector representations using language embedding models (e.g., OpenAI). The system should leverage technologies such as Langchain and OpenAI for efficient data retrieval, and should store these vectors in a suitable vector database like Pinecone. The size of documents within each collection may vary between 10 to 700 pages, and each collection may comprise between 1 to 25,000 documents - possibly more. Post vectorization, a user-friendly web-based interface will be provided to allow interactions with the document collections. The system should also be able to remove documents and embeddings related to these documents. This project encompasses several core components that will need to be developed: 1. Document Upload and Collection Creation The front-end interface should provide functionality for users to upload documents and create new collections. It should also enable monitoring the status of the ongoing embeddings. The system should be designed to handle large volumes of data, given the potential size and quantity of documents in each collection. 2. Document Parsing The system should be able to parse and preprocess the input data files (PDF or text). It will involve text extraction, data cleaning, and possibly segmenting the documents into manageable chunks for processing. 3. Vectorization and Embedding The preprocessed data should be fed into an OpenAI model or another suitable language model to generate vector representations of the content. This will likely involve handling and optimizing large-scale machine learning tasks. 4. Data Storage and Retrieval The generated vectors need to be stored in a vector database such as Pinecone. The system should be optimized for efficient data retrieval, likely involving indexing and query operations with Langchain and OpenAI. 5. Chat Interface A conversational interface should be developed to enable users to interact with the document collections. This will require natural language processing capabilities and should either retain the context of the ongoing conversation or allow the user to initiate a new conversation with a collection. 6. System Integration and Testing The system's different components need to be fully integrated and thoroughly tested to ensure the system functions as expected and can handle the anticipated data volume. It's important that the system is robust, reliable, and can scale up to meet increasing demand. Skills Required: - Strong proficiency in Python, NodeJS, PHP and web development languages such as JavaScript, HTML/CSS. Extensive experience with natural language processing, particularly in relation to language models such as GPT-3 or similar. - Experience with machine learning model deployment and management. - Familiarity with document parsing libraries and tools. - Experience with databases, specifically vector databases such as Pinecone. - Knowledge of Langchain and OpenAI's tools and APIs. - Proficiency in developing interactive web-based user interfaces. - Strong system integration and testing skills. IMPORTANT - DO NOT BID PRIOR TO READING THE BELOW: Applicants should have experience in developing similar systems, including document parsing, vectorization, natural language processing, vector databases, web interface development, and system integration. Examples of past relevant projects are necessary. Bids should include a high-level development approach detailing the technical stack, system design and architecture, strategies for each core component, and measures for system robustness and scalability. The bid should account for regular consultations, comprehensive documentation, including installation and user guides, and 15 days of post-implementation support for issues, tweaks, or minor feature additions.